We found that questions generated by the above model often copy parts of the answer sentence, including information that is introduced for the first time in the answer sentence. For example, in Figure REF , Hurricane Hugo is first mentioned in sentence 3. The model might ask “What type of relief is going to California and regions affected by Hurricane Hugo?” This makes the question prone to “foresee” details that are unlikely to be inferred from previous context, violating QUD principles.
We observe that these unwanted details often pertain to specific entities. To this end, in the answer sentence, we
replace each token that belongs to a named entity with its entity type before feeding into the GPT-2 model.We use the bert-base-NER model trained on the CoNLL-2003 NER dataset [1]}
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